This talk presents how one should draft a paper with a clear logic in a step-by-step manner that is disclosed nowhere else. Slides, which are useful for drafting a paper, not to mention presentation, are first prepared, assuming a 10-15 minute presentation. It helps the author build a solid logic for the paper, which directly benefits the author(s) by making reviewers understand the point of the paper easily and clearly, leading to a higher review score. There is a specific order of preparation to make the process most efficient. The highlight is a three point analysis for which templates are provided to clarify the value, the trick, and the user benefit of the paper. These three points naturally lead to a good paper title that can also be used in the abstract. Some useful tips are scattered in the talk.

Unlike books and lectures which tell what to do, this tutorial teaches what to do in what order with original templates. The speaker has research experiences in industry for over 35 years and has supervised many junior engineers and 75 internship students from different countries most of who wrote a Master’s thesis on the internship topic. He is also a regular presenter at major conferences such as ICASSP and ICCE. Based on his own writing experience in addition to what he learned in a technical writing course in his early days, he has developed a comprehensive way to technical writing. He has been talking about the way in recent invited tutorial talks which have been attended very well. He is an engaging speaker and was appointed as a Distinguished Lecturer by IEEE Signal Processing Society and Consumer Electronics Society as well as a Distinguished Industry Speaker by SPS.

The rationale of this workshop is to provide an overview of some of the recent developments and activities in radar sensing and related signal processing from the diverse Dutch research community under the “Nederland Radarland” framework, but not limited to that.

Specifically, the workshop will look at the two emerging topics of automatic target classification using radar signatures and micro-Doppler processing, and radar signal processing for high-resolution radar. Based on the information provided by micro-Doppler signatures, several processing techniques have been developed, either based on available target models or on the definition and extraction of handcrafted features. However, these “model-driven” approaches have been very recently challenged by the explosion of methods based on artificial intelligence and deep learning, often inspired from work by the image and audio processing community.

Interesting research questions arise from the application of “data-driven” approaches to the problems of radar-based target classification. What is the best domain/format of radar data for classification in a given application? What is the best neural network architecture to work with radar data which are neither images nor speech or audio? How to get enough radar data to train deep neural networks and how to make their decision process fully explainable?

And the list of outstanding research questions could continue, and intersect similar trends in other disciplines such as audio processing, image processing, and biomedical signal processing where techniques to identify and classify classes/entities of interest from the data are a significant topic of interest, with exciting opportunities of collaborations across research domains and applications.

To promote mutual understanding and collaborations across research communities, it is desirable to have an overview of the state of the art of available techniques and related results. This workshop will provide an overview of the fundamentals of radar micro-Doppler signal processing, and its applications in a diverse range of scenarios.

The workshop is designed as a specific focused session involving radar sensing, aiming to attract interest to topics which may not be necessarily extensively represented in the main conference sessions. Four talks are expected within the workshop as detailed in the next page, for approximately a half day schedule including a middle break and time for Q&A between audience and speakers.

The expected audience includes engineers and researchers with an academic or industrial background, as well as researchers from governmental/defence organisations with some interest in radar sensing. The talks within the workshop have been designed to start from the fundamentals concepts and to lead then to some more advanced techniques and applications, in order to facilitate participations also from attendees without an expert radar background.

Outline and abstract of talks:

1) Radar micro-Doppler signatures

Jacco de Wit, TNO, The Netherlands

The radar micro-Doppler signature of a target depends on its micro-motion, i.e., the motion of parts of a target relative to the motion of the target as a whole. These micro-motions are very characteristic for individual target classes, e.g., the relatively slow pendulum-like motion of a bird’s wings or a human’s legs versus the rapidly rotating propellers of a small fixed-wing or rotary-wing aircraft. Consequently, target classification is the most eminent application for radar micro-Doppler signatures.

In this talk, the nature of the micro-Doppler characteristics of different target classes will be discussed. How these characteristics are captured in the radar micro-Doppler signature, depends on the radar waveform, the processing parameters and the chosen (time-frequency) representation.

The impact of the different choices on the final signature will be highlighted with measured examples of micro-Doppler characteristics. What the best choices depend on the classification problem and the type of classifier applied. To illustrate this, three main types of classifiers will be treated: data-driven classifiers, model-based classifiers and the more conventional classifiers based on (hand-crafted) features.

In the framework of non-cooperative target recognition, micro-Doppler signatures observable in radar measurements are recognized as a powerful source of information that can be exploited for several applications, among which target classification represents the most prominent. Furthermore, there are many approaches to extract relevant information from these signatures and radar data, i.e. recognizing what information is mostly sought after and what are the best processing approaches to obtain such information.

This talk aims to expand on the general introduction to radar micro-Doppler signatures in the previous talk, and present a variety of typical use cases and processing algorithms that allow extracting the associated information of interest, ranging from model-driven techniques to deep learning algorithms.

Radar classification based on micro-Doppler requires a long integration time, resulting in a waveform that can take up a large part of the radar (time) resources. For this reason, these types of modes are not used regularly in surveillance radar systems.

However, if such classification modes could be interrupted and interleaved with other modes, they might become more acceptable. For instance, for radar systems using phased array antennas where the beam can be steered instantaneously, the time between track updates for one target could be used for classification of another target using micro-Doppler waveforms. The resulting total waveform consists then of interleaving of radar modes.

Compressive Sensing (CS) is a novel signal acquisition and processing technique which enables reconstruction of sparse signals from a set of highly undersampled linear measurements. In many radar applications, such as air traffic control, obstacle avoidance, and wide-area surveillance, it is fair to assume that the scene is sparse, i.e., the number of targets is much smaller than the number of resolution cells in the search area or volume. Compressive Sensing techniques could be used in radar systems where (some of) the waveforms are interrupted to enable interleaving of several radar modes. This way, several modes could be executed simultaneously.

For the specific purpose of classification using micro-Doppler, we are interested in considering how CS can be used to recover high-resolution Doppler spectrograms from measurements with missing data. During the time in which the classification waveform is interrupted, leading to missing data, other waveforms could be transmitted by the radar. If the missing data can be recovered by means of Sparse Signal Processing, the resulting spectrograms can further be used for the classification of targets such as drones, birds and human gaits.

The classification of object behavior is of interest in applications like maritime surveillance, automotive and healthcare. In this presentation, a generic framework is outlined where prior knowledge is exploited via parameterized motion and observation models where the unknown parameters are learned from the measured data. This has several advantages over non-hybrid learning with respect to the number of required measurements and the flexibility in the type of sensors involved.

The workshop on Artificial Intelligence for Health aims at bringing together researchers from academia, industry, government and medical centers in order to present the state of the art and discuss the latest advances in the emerging area of the use of Artificial Intelligence and Soft Computing techniques, be they black boxes or explanation-based, to signals, images, and data in the fields of medicine, healthcare and wellbeing.

AI4Health is expected to cover the whole range of theoretical and practical aspects, technologies and systems related to the application of artificial intelligence and soft computing methodologies to issues as machine learning, deep learning, knowledge discovery, decision support, regression, forecasting, optimization and feature selection in the healthcare and wellbeing domain.